Method and apparatus for pattern inspection

ABSTRACT

According to the present invention, for a pattern inspection apparatus that compares images in corresponding areas of two patterns that are identical and that determines an unmatched portion between the images is a defect, a plurality of detection systems and a plurality of corresponding image comparison methods are provided. With this configuration, the affect of uneven brightnesses for a pattern that occurs due to differences in film thicknesses can be reduced, a highly sensitive pattern inspection can be performed, a variety of defects can be revealed, and the pattern inspection apparatus can be applied for processing performed within a wide range. Furthermore, the pattern inspection apparatus also includes a unit for converting the tone of image signals of comparison images for a plurality of different processing units, and when a difference in brightness occurs in the same pattern of the images, a defect can be correctly detected.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 11/119,944, filed May 3, 2005, the contents of which areincorporated herein by reference.

INCORPORATION BY REFERENCE

The present application claims priority from Japanese applicationJP2004-138009 filed on May 7, 2004, the content of which is herebyincorporated by reference into this application.

BACKGROUND OF THE INVENTION

The present invention relates to pattern inspections according to whichobject images obtained using lights, or laser beams, are compared withreference images, and based on comparison differences, minute patterndefects or substances are found. The present invention particularlyrelates to a pattern inspection apparatus, and a method therefor, thatis appropriate for conducting visual inspections of semiconductorwafers, TFTs and photomasks.

A method disclosed in JP-A-05-264467 is a known conventional techniquefor comparing an object image to be inspected with a reference image,and for detecting pattern defects.

According to this method, inspection samples wherein a repeated patternis regularly arranged are sequentially scanned by a line sensor, and theobtained image is compared with an image obtained with a delayequivalent to the repeated pattern pitch. An unmatched portion isdetected as a pattern defect. This conventional inspection method willbe described while employing, as an example, the visual inspection of asemiconductor wafer. As is shown in FIG. 6, multiple chips having thesame pattern are arranged in a semiconductor wafer to be inspected. Eachof these chips can be roughly classified into a memory mat portion 71and a peripheral circuit portion 72, as is shown in FIG. 7. The memorymat portion 71 is a set of small, repeated patterns (cells), while theperipheral circuit portion 72 is basically a set of random patterns.Generally, the memory mat portion 71 has a high pattern density, and adark image is obtained by a bright-field illumination optical system.However, since the peripheral circuit portion 72 has a low patterndensity, a bright image is obtained.

During a conventional visual inspection, images located at the samepositions in the peripheral circuit portions 72 of adjacent chips, e.g.,images in areas 61 and 62, etc., are compared, and a difference in thetwo is detected as a defect. At this time, since the two images are notalways aligned, due to the vibration of a stage or the tilting of anobject, a position shift distance is calculated between an imageobtained by a sensor and an image that has been delayed by a timeequivalent to a repeated pattern pitch. Registration of the two imagesis performed based on the obtained position shift distance, and adifference between the images is calculated. When the difference isgreater than a predetermined value, this is determined to be a defect,or when the difference is smaller, this is determined to be anon-defect.

Generally in the registration of the two images during the comparisonprocessing, the edges of the images are employed as a set of informationfor calculation of the position shift distance, and the position shiftdistance is calculated so that misalignment of the patterns in theimages is minimized. Actually, a method for using normalizedcross-correlation or a method using a residual sum is proposed.

SUMMARY OF THE INVENTION

Further, in a semiconductor wafer to be inspected, there are slightdifferences in the film thickness of the pattern due to theplanarization produced, for example, by the CMP method, and for chips,the brightnesses of the images differ locally. For example, referencenumeral 41 in FIG. 4A denotes an inspection image and reference numeral42 in FIG. 4B denotes an example reference image, and as indicated by 4a and 4 b in FIGS. 4A and 4B, the brightnesses of the inspection imageand the reference image differ, even though the patterns are the same.Further, in FIG. 4A, there is a defect 4 d in 41 in FIG. 4A for theinspection image. In this case, a differential image is as shown in FIG.4C. The differential image is an image representing light and shade inaccordance with a difference between the inspection image and thereference image at each position. The waveform of a difference at aposition 1D-1D′ is as shown in FIG. 4D. Since according to theconventional method a portion wherein the differential value is equal toor greater than a specified threshold value TH is regarded as a defect,a differential value 4 c between the pattern 4 a and the pattern 4 b,which have different brightnesses, is detected as a defect. This shouldnot, however, be detected as a defect, i.e., this is a false defect.Therefore, as a conventional method for avoiding the occurrence of afalse defect, such as 4 c in FIG. 4C, the threshold value TH isincreased (TH in FIG. 4D→TH2). However, increasing the threshold valueTH will reduce inspection sensitivity, and a defect 4 d having adifferential value equal to or less than the threshold value will not bedetected.

Furthermore, there is a case wherein differences in brightness occur duemerely to differences in the film thicknesses of specified chips in achip array on a wafer shown in FIG. 6, or only in a specified pattern ina chip. If the threshold value TH is adjusted in consonance with theselocal areas, the overall inspection sensitivity would be considerablyreduced.

In addition, when, as shown in FIG. 4, the difference in brightness dueto the interference of a thin film is great in one part of the pattern,a correct position shift distance may not be obtained by using either amethod employing the normalized cross-correlation or a method forcalculating a residual sum.

On the other hand, there is a case wherein a defect can be revealed by acombination of factors, such as the material of an object, the surfaceroughness and the size or the depth, that depend on the object, and afactor, such as an illumination condition, that depends on a detectionsystem.

The present invention resolves these conventional inspection technicalproblems and provides a pattern inspection method, for a patterninspection apparatus that compares images in corresponding areas for twocorresponding patterns and that determines that an unmatched imageportion is a defect, whereby uneven brightnesses of the patterns that iscaused by differences in film thicknesses is reduced and a highlysensitive pattern inspection can be conducted, and also provides apattern inspection apparatus.

The present invention also provides a pattern inspection method, wherebya plurality of switchable detection system units (combinations ofillumination optical systems and detection optical systems) arearranged, whereby a detection system (a combination of an illuminationoptical system and a detection optical system) is selected in accordancewith an object or a target defect, and whereby the inspection isperformed by a corresponding comparison and inspection method, so that ahighly sensitive pattern inspection that can cope with a greater varietyof defects can be attached, and a pattern inspection apparatus.

That is, according to the invention, a pattern inspection apparatus,which compares images in corresponding areas for two correspondingpatterns and which determines that an unmatched image portion is adefect, comprises: a plurality of different switchable detectionsystems; a plurality of image comparison systems; and a plurality ofdefect categorizing systems that are consonant with the detectionsystems. When one or more detection systems are selected, the detectionfunctions of the corresponding image comparison processing system andthe defect categorization system should be taken into account. With thisarrangement, an optimal condition can be selected, and a variety oftypes of defects can be detected.

A pattern inspection apparatus further comprises a unit for convertingtones of image signals for compared images using a plurality ofdifferent processing units. With this arrangement, when an inspectionobject is a semiconductor wafer, and when the brightness differs in thesame patterns of images due to a difference in the film thickness of thewafer, a fluctuation in the amount of illuminating light, discrepanciesin pixels associated with the sensitivity of an image sensor, or unevenaccumulated periods for the amount of light, a defect can be correctlydetected.

Furthermore, a unit for sequentially adjusting position shifts of thecompared images and brightness differences between the compared images,and a unit for adjusting the position shifts and the brightness shiftsat the same time are provided. With this arrangement, when a greatdifference in brightness occurs, in a specific pattern, between images,due to a difference in the film thickness of the wafer, the positionshift can be accurately detected. Furthermore, when a position shiftoccurs between images, the brightness difference can be accuratelydetected.

According to the present invention, since a plurality of imagecomparison processes and a plurality of categorizing processescorresponding to a plurality of detection systems are preformed, ahighly sensitive inspection is ensured, and various defects can bedetected.

In addition, brightnesses at different levels for images to be compared,which occur due to differences (uneven colors) in brightness betweenchips that are the results of various factors, such as differencesbetween chips in film thicknesses, differences in the amount of lightaccumulated caused by variations in the speed of the stage, orfluctuations in illumination, are adjusted using a plurality ofdifferent methods. As a result, a defect for which a weak signal ishidden by strong, uneven brightness can become noticeable and can bedetected.

Moreover, when the calculation of a position shift distance robustenough for uneven colors is performed, a more highly sensitiveinspection can be made.

These and other objects, features and advantages of the invention willbe apparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example configuration for aninspection apparatus;

FIG. 2 is a front view of an example inspection apparatus including aplurality of illumination systems;

FIG. 3 is a diagram showing an example plurality of image comparisonmethods;

FIGS. 4A, 4B, 4C and 4D are diagrams respectively showing an objectimage for inspection, a reference image, a differential image and thewaveform of the differential image at a position D-D′;

FIG. 5 is a plan view of a semiconductor wafer that is an example imagecomparison processing unit;

FIG. 6 is a plan view of a semiconductor wafer that is an array of chipsto be inspected;

FIG. 7 is a plan view of an example structure for a chip;

FIG. 8 is a diagram showing a method for selecting a detection systemand a comparison method;

FIG. 9A is a diagram showing images, contrasts, density differences,luminance distributions and S/N for individual detection systems fortarget defects that are designated;

FIG. 9B is a diagram showing images, contrasts, brightnesses,differential values and luminance distributions for individual detectionsystems for target areas;

FIG. 10 is a flowchart showing example detection system setupprocessing;

FIGS. 11A and 11B are diagrams showing example displays of inspectionresults obtained by a plurality of detection systems, comparison methodsand categorizing methods;

FIG. 12 is a flowchart showing example processing for correcting unevenbrightnesses that occur in individual areas;

FIG. 13 is a flowchart showing example processing performed by acorrection value calculator 108;

FIG. 14 is a flowchart showing example processing for correcting unevenbrightnesses that occur at random;

FIG. 15A is a diagram showing a reference image and a detected image;

FIG. 15B is a scatter diagram for an entire image;

FIG. 15C is a graph showing a two-dimensional feature space;

FIG. 15D is a scatter diagram showing one segment obtained by dividing afeature space based on the scatter diagram for the entire space;

FIG. 15E is a scatter diagram showing one segment obtained by dividing afeature space based on the scatter diagram for the entire space;

FIG. 16A consists of graphs showing relationships between brightness,which represents an example segment division of a feature space, and adensity difference and frequency;

FIG. 16B consists of graphs showing relationships between brightness,which represents an example segment division of a feature space, and adensity difference and frequency;

FIG. 17 is a flowchart showing the processing for dividing a featurespace into segments;

FIG. 18A is a diagram showing a differential image after registrationhas been performed;

FIG. 18B is a diagram showing a differential image after unevenbrightnesses in areas have been corrected through registration;

FIG. 18C is a diagram showing a differential image after unevenbrightnesses for pixels have been corrected through registration;

FIG. 18D is a graph showing distributions of luminance values anddifference values for a reference image and a detected image at 1D-1D′in FIG. 18A;

FIG. 18E is a graph showing distributions of luminance values anddifference values for a reference image and a detected image in FIG.18B;

FIGS. 19A, 19B and 19C are diagrams showing a detected image, areference image and a differential image;

FIG. 19D is a scatter diagram for a detected image and a referenceimage;

FIG. 20A is a diagram showing a detected image wherein unevenbrightnesses exist;

FIGS. 20B and 20C are diagrams showing a reference image and adifferential image;

FIG. 20D is a scatter diagram for a detected image and a referenceimage;

FIG. 20E is a diagram showing a correlation between FIG. 20A and FIG.20B;

FIGS. 21A and 21B are diagrams showing example processing forregistration relative to large uneven brightnesses, and for correctionof brightness;

FIG. 22 is a flowchart showing example registration and brightnesscorrection processing relative to large uneven brightnesses;

FIG. 23 is a flowchart showing example registration and brightnesscorrection processing for large uneven brightness;

FIG. 24 is a flowchart showing example registration and brightnesscorrection processing for large uneven brightness;

FIG. 25A is a scatter diagram for brightnesses of a detected image and areference image;

FIG. 25B is a graph showing positive correlation distribution dataobtained by Hough conversion of the scatter diagram;

FIG. 25C is a graph showing the first negative correlation distributiondata obtained by Hough conversion of the scatter diagram;

FIG. 25D is a graph showing the second negative correlation distributiondata obtained by Hough conversion of the scatter diagram;

FIG. 26 is a diagram showing the optimal set of functions for an imagecomparison method and the processing order thereof;

FIGS. 27A, 27B and 27C are diagrams showing a detected image, areference image and a differential image;

FIG. 27D is a scatter diagram for a detected image and a referenceimage;

FIG. 27E is a diagram showing the division of the scatter diagram forindividual feature values;

FIG. 27F is a scatter diagram for a detected image and a reference imagefor which brightnesses have been corrected in accordance with correctionvalues for individual pixels that are obtained based on the scatterdiagrams of the individual feature values;

FIG. 27G is a diagram showing a differential image obtained from thedetected image and the reference image for which the brightnesses havebeen corrected using the correction values for the pixels;

(a), (b) and (c) in FIG. 28 are a detected image, a reference image anda differential image;

(d) in FIG. 28 is a scatter diagram for the detected image and thereference image;

(e) in FIG. 28 is a differential image;

(f) in FIG. 28 is a diagram showing correction of brightness for eacharea and correction of brightness for each pixel;

FIGS. 29A and 29B are diagrams showing example registration andbrightness correction processing relative to large uneven brightnesses;

FIG. 30 is a flowchart showing example registration and brightnesscorrection processing relative to large uneven brightnesses;

FIG. 31 is a block diagram showing a semiconductor manufacturing systemaccording to the present invention;

FIG. 32 is a flowchart showing example registration and brightnesscorrection processing relative to large uneven brightnesses; and

FIG. 33 is a flowchart showing example registration and brightnesscorrection processing relative to large uneven brightnesses.

DESCRIPTION OF THE EMBODIMENTS

One embodiment of the present invention will now be described in detailwhile referring to FIGS. 1 to 33.

For the embodiment, a defect inspection method for an optical visualinspection apparatus using a semiconductor wafer as an object isemployed as an example. An example configuration for the optical visualinspection apparatus is shown in FIG. 1. Reference numeral 11 denotes asample (an object to be inspected, such as a semiconductor wafer); 12, astage on which the sample is mounted and moved; and 13, a detector.

The detector 13 includes: a light source 101, for irradiating the sample11; an illumination optical system 102, for condensing light emitted bythe light source 101; an object lens 103, for irradiating the sample 11with illuminating light condensed by the illumination optical system 102and for forming an optical image by focusing the light reflected by thesample 11; an image sensor 104, for receiving the optical image and forchanging the optical image into an image signal consonant with thebrightness; and an AD converter 105, for converting a signal receivedfrom the image sensor 104 into a digital signal.

In the example shown in FIG. 1, a lamp is employed as the light source101; however, a laser may also be employed. Further, light emitted bythe light source 101 may be either light having a short wavelength orlight for which the wavelength range lies within a large bandwidth(white light). As shortwave light, light having a wavelength in theultraviolet range, i.e., ultraviolet light (UV light), can also beemployed in order to increase the resolution of an image to be detected(for detecting a minute defect). When a shortwave laser is employed as alight source, a unit (not shown) for reducing coherence should beprovided.

A time delay integration image sensor (TDI sensor), wherein a pluralityof one-dimensional image sensors are arranged two-dimensionally, can beemployed as the image sensor 104. In synchronization with the movementof the stage 13, signals detected by the individual one-dimensionalimage sensors are transmitted to the one-dimensional image sensors atthe following step and are added together, so that comparatively fastand highly sensitive detection is enabled.

Reference numeral 14 denotes an image editing unit 14, which includes: apre-processor 106, for performing image correction, such as shadingcorrection or dark level correction, for an digital image signalobtained by the detector 13; and an image memory 107, in which thecorrected digital image signal is stored.

Reference numeral 15 denotes an image comparator for calculating defectcandidates in a wafer serving as a sample. The image comparator 15compares two images (a detected image and a reference image) stored inthe image memory 107 of the image editing unit 14, and determines that aportion whose difference is greater than a threshold value is a defect.First, the image comparator 15 reads digital signals for a detectedsignal and a reference signal stored in the image memory 107; acorrection value calculator 108 calculates correction values forcorrecting a position and brightness; and an image comparator 109employs the obtained correction values for the position and thebrightness to compare the brightnesses of the detected image and thereference image at the corresponding position and outputs as defectcandidates portions for which the differential value is greater than aspecific threshold value. A parameter setup unit 110 sets up an imageprocessing parameter, such as a threshold value for extracting a defectcandidate from a differential value, and transmits this parameter to theimage comparator 109. Then, a defect categorizing unit 111 extracts atrue defect, based on the feature values of the individual defectcandidates, and categorizes the defect.

Reference numeral 16 denotes an overall controller that includes a userinterface 112, which includes an input unit that accepts a change for aninspection parameter (e.g., a threshold value used for image comparison)entered by a user and a display unit for displaying detected defectinformation, a storage device 113, in which the feature values ofdetected defect candidates and images are stored, and a CPU(incorporated in the overall controller 160), for performing variouscontrol processes. Reference numeral 114 denotes a mechanical controllerthat drives the stage 12 based on control instructions issued by theoverall controller 16. The image comparator 15 and the detector 13 arealso driven in accordance with instructions issued by the overallcontroller 16.

In a semiconductor wafer 11 to be inspected, multiple chips having thesame pattern are arranged regularly, as shown in FIG. 6. According tothe inspection apparatus in FIG. 1, the overall controller 16sequentially moves the semiconductor wafer 11, which is a sample mountedon the stage 12, and in synchronization with this movement, fetchesimages of the chips from the detector 13. Then, while employing theabove described procedures, the overall controller 16 compares theimages detected at the same positions for two adjacent chips, e.g.,compares the digital image signals for areas 61 and 62 in FIG. 6, whichare regarded as a detected image and a reference image, and detects adefect.

The detector 13 of the inspection apparatus for this embodiment includesa plurality of switchable detection systems. The detailed arrangement ofthe detector 13 is shown in FIG. 2.

Light emitted by the light source 101 passes through an artificialcontinuation optical system 3210, and enters a light path branchingoptical system 23, while the amount of light is averaged orsubstantially smoothed along the time axis. The light source 101 is alaser, and light emitted is ultraviolet light (UV light, or deepultraviolet light (DUV light)), or visible light. The polarization oflight incident to the light path branching optical system 23 is thenadjusted by a polarization unit 2302, and thereafter, light is branched,by a polarized beam splitter 2301, into two light paths 2601 and 2602.The polarization unit 2302 is formed, for example, of a rotatable ½ waveplate, and the ratio of the amount of P polarized light to the amount ofS polarized light to be transmitted can be adjusted within a range offrom 1:0 to 0:1 in accordance with the rotation angle of the ½ waveplate. Light branched to the light path 2601 enters a beam formationoptical system 201, wherein the diameter of the beam and the illuminancedistribution are adjusted. Then, while the light path is bent by amirror 202, light enters a coherent reduction optical system 203,wherein time and spatial coherences are reduced. The illuminating lightfrom the coherence reduction optical system 203 is transmitted to amodified illumination optical system 20, whereat the illuminancedistribution at the pupil position of the object lens 10 is changed, andirradiates the wafer 11 through a light modulation unit 21 and theobject lens 103. In the following explanation, the illumination usinglight transmitted along the above described light path, i.e., theillumination through the object lens 103, is called bright-fieldillumination.

When the modified illumination optical system 20 is used to change theilluminance distribution of the light at the pupil position of theobject lens 103 to a plurality of types, illumination using light thatis branched to the light path 2601 in the above described manner can beperformed to cope with the handling of wafers using a variety ofprocesses. The modified illumination optical system 20 can, for example,be a filter for which light transmittance is changed in the light axialcross section, or an optical device that forms four luminous fluxes oreight luminous fluxes with point symmetry at a light axis. Further, adevice that can move a beam may be employed to change the position ofthe beam. A device that can be used for moving the beam is a galvanomirror, for example, or a semiconductor resonance mirror. The modifiedillumination optical system 20 is so designed that it can switch thesemirrors.

Light that is branched to the light path 2602 by the beam splitter 2301passes through the interference reduction optical system 203 and enters,thereafter, a polarized light dark-field illumination optical systemA24. The light is then branched by a partial mirror 2401 to provide twolight paths. Light transmitted along one path passes through opticaldevices 2403 and 2405 and enters a polarization dark-field illuminationoptical system B25, while light transmitted along the other path isreflected by a full reflection mirror 2402 and passes through theoptical devices 2403 and 2405 and enters the polarization dark-fieldillumination optical system B25. Light incident to the polarizationdark-field illumination optical system B25 passes through optical device2501 or 2502 and is reflected by a mirror 2503 or 2504. Thereafter, itis emitted obliquely, onto the surface of the wafer 11.

Of the reflected light that passes along the light path 2601 or 2602 andis emitted onto the wafer 11, light condensed by the object lens 103passes through the light modulation unit 21, the polarized beam splitter27 and the light modulation unit 22, and is focused on the detectionface of the image sensor 104. The image sensor 104 detects the opticalimage that is thus formed and the A/D converter 105 converts thedetection signal output by the image sensor 104 to obtain a digitalsignal that the detector 14 outputs. At this time, the image sensor 104outputs, in parallel, a plurality of detection signals, and the A/Dconverter 105 performs the parallel A/D conversion of the detectionsignals and the parallel output of digital signals.

The light modulation unit 21 controls the illumination provided by thelight that is branched to the light path 2601, and the amount of lightreflected by the wafer 11 and the phase of the reflected light. Forexample, the light modulation unit 21 either adjusts the ratio of theamount of zero-order diffraction light reflected by the wafer 11 inaccordance with the amount of high-order diffraction light or employspolarization differential interference, so as to improve the contrastfor a circuit pattern signal detected by the image sensor 104. In thiscase, for the adjustment of the ratio of the zero-order diffractionlight and the high-order diffraction light, a ½ wave plate and a ¼ waveplate must be combined to change the vibration direction of light. Forthe polarization differential interference, only a birefringence prismneed be provided. A physical phenomenon produced by a polarizationdifferential interference optical system that employs one Nomarski prismis the same as that for the common differential interference microscope.These components can also be employed by switching.

When the above described light modulation unit 22 is arranged at alocation equivalent to the pupil position of the object lens 103,optical modulation at the pupil position is enabled. For example, amaterial on which a dielectric film is deposited is placed in the centerof a transparent substrate, such as one composed of a vitreous silica,and light detected by the image sensor 104 is changed by altering thetransmittance at the dielectric film portion. Instead of the dielectricfilm, a unit shielded by metal, for example, may be employed. Also Forthis unit, the above described wavelength plates and the birefringenceprism can be switched.

Furthermore, the polarized light dark-field illumination optical system24 and the polarized light dark-field illumination optical system 25emit illuminating light onto the wafer 11 from outside the object lens103. Along the light path 2602 in the polarized light dark-fieldillumination optical system 24, a partial mirror 2401 for providinglight paths and a full reflection mirror 2402 are arranged. Whereas,illuminating light that is branched to the light path 2601 by the lightpath branching optical system 23, i.e., illuminating light passedthrough the object lens 103 is called bright-field illuminating light.

Since the light modulation unit and the function that performs, forexample, the modified illumination and the dark-field illumination atthe same time as the bright-field illumination is provided, an optimaloptical system can be selected in accordance with the type of defect tobe detected and an optimal inspection can be conducted.

The image comparator 15 also comprises a plurality of processorscorresponding to the detection systems.

The arrangement of the image comparator 15 is shown in FIG. 3. The imagecomparator 15 includes a plurality of methods (1501 to 1503 in theexample in FIG. 3) for individual functions, such as an image editionfunction, a position correction coefficient calculation function method,and a brightness correction coefficient calculation method. When a setof detection systems has been determined, the corresponding optimalcombination of functions and the order of processes are determined. Forexample, according to the image comparison method 1501, defectcandidates are extracted by 108-1 and 109-1, and a defect is detectedand categorized by 111-1. At this time, a corresponding value is set asan image parameter by 110-1.

As the optimal combination of the individual functions and the order ofprocesses, example comparison processing is shown in FIG. 26, whereincalculation of a position correction coefficient using a correlationcoefficient→image editing through smoothing in the direction of theheight of an edge (smoothing of the amount of signals when a slightdifference in the direction of the height of the sample 11 exists in animage)→calculation of a brightness correction coefficient using acontrast and brightness are performed in order. There are various othercombinations and calculation orders, such as no image edition→collectivecalculation of a position correction coefficient and a brightnesscorrection coefficient. This can be applied for the categorizingprocessing.

As is described above, a sample to be inspected is photographed under anoptimal detection condition selected from among a plurality of detectionconditions, and images are compared based on an image comparison methodand a defect categorizing method corresponding to the detectioncondition. Then, a highly sensitive inspection is ensured, and a varietyof types of defects can be detected.

The optimal detection system selection method will now be described. Asdenoted by 801 in FIG. 8, first, information for a sample that is anobject is input. For a semiconductor wafer, a step, a target area (e.g.,a memory mat portion) and the coordinates of a target defect, if adefect as desired is already known, are designated. Then, a detectionsystem is changed, and the images of the target area and the targetdefect are photographed, and an image contrast, a luminance value, thedirection of a pattern, a pattern density, and a difference (S/N) inluminance values of the defect and the peripheral portions arecalculated, and the obtained values are displayed on the screen, asshown in FIGS. 9A and 9B. The images, the contrasts, the densitydifferences, the luminance distributions and the S/Ns obtained by theindividual detection systems for the designated target defect are shownin the example in FIG. 9A. The images, the contrasts, the brightnesses,the differential values and the luminance distributions obtained by thedetection systems for the target area are shown in the example in FIG.9B. As a result, one or more conditions can be selected. A user mayselect the condition while monitoring the images and evaluation values,as displayed in FIGS. 9A and 9B; or the condition may be automaticallyselected from among the evaluation values. And, in accordance with theselected detection system, as denoted by 802 in FIG. 8, the inspectionis conducted using the comparison method and the categorizing methodthat correspond to the detection system.

FIG. 10 is a flowchart showing the processing for selecting a detectionsystem based on a more detailed evaluation. First, a detection system isset (S101), an image is photographed (S102), an image comparison methodcorresponding to the detection system is set (S103), and a testinspection is performed (104). This processing is repeated for severaldetection systems. When a target defect is found by one of detectionsystems, the coordinates of the defect are designated (S105). A useralso designates the coordinates of an area, such as a memory matportion, to be highly sensitively inspected (S106). When a target defectto be detected is already known, this test inspection need not beperformed. Then, the defects designated by the detection systems and theimages in the designated areas are obtained (S107 to S109), aquantitative evaluation value shown in FIGS. 9A and 9B is calculated andthe detection results are displayed (S110). Further, a test inspectionis conducted using the image comparison method that corresponds to thedetection system that has been set, and the obtained results aredisplayed (S111). Thereafter, while taking into account the quantitativeevaluation values of images obtained by the two detection systems, i.e.,the different detection systems, and detection functions using thedifferent image comparison methods, one or more optimal conditions areselected (S112). For the detection results while taking into account theimage quantitative evaluation value and the function of the imagecomparison method, the same weight may be employed, or either theevaluation value or the function may be weighed. As a result, theoptimal detection systems are selected (S113), and corresponding imagecomparison methods and corresponding categorizing methods are selected(S114). Thereafter, inspections are performed under all the selectedinspection conditions (S115), and when the inspections have beencompleted under all the selected conditions (S116), the results aredisplayed in a map form on the display screen (S117).

In FIGS. 11A and 11B are shown one or more selected detection systems,and defect detection and categorization results obtained by thecorresponding comparison methods and the corresponding categorizingmethods. The inspection results obtained by a plurality of detectionsystems can be displayed for the individual systems, as denoted by 1101.Or, as denoted by 1102, the logical product or the logical sum of thedetection results obtained by the detection results may be calculated,and the inspection results may be synthesized and displayed. As thedetection results, the presence or the absence of a detected defect maybe displayed as a map; however, as denoted by 1101 and 1102, when thecategorization results are displayed on a map, a user can understand ata glance the best condition wherein a target defect can be detected.

Next, example processing performed by the image comparator 15 will nowbe described in detail while referring to FIG. 13. First, the imagecomparator 15 reads a detected image signal and a reference image signalthat are sequentially input to the memory 107 in synchronization withthe movement of the stage. These image signals for two chips are signalsnot exactly at the same locations due to the vibrations of the stage andthe inclinations of the wafer mounted on the stage. Therefore, thecorrection value calculator 108 normally calculates a position shiftdistance between two images (S1081). The calculation of a position shiftdistance is performed sequentially by using, as one processing unit, aspecific length in the direction in which the stage advances. Referencenumerals 51, 52, . . . in FIG. 5 denote processing areas when length D(a pixel) is defined as one processing unit. Hereinafter, the processingarea of this unit is described as a unit.

Relative to an input image, the position shift distance is calculatedsequentially between individual units, such as the unit 51 and the unitof a corresponding adjacent chip, and then the unit 52 and the unit of acorresponding adjacent chip. In order to calculate a position shiftdistance, there are various methods, such as a method employing anormalized cross-correlation between images, a method for employing thesum of density differences between images and a method using the squaresum of density differences between images, and one of these methods isselected. Then, based on the obtained position shift distance,registration of the two images is performed for each unit (S1082).Sequentially, for the two images that are aligned, a correction valuefor adjustment of uneven brightness is calculated at two steps. Thefactors that cause a difference in brightness are slight differences inthe film thicknesses of the chips in the semiconductor wafer andfluctuations in the amount of illuminated light. The uneven brightnessdue to the slight differences in the film thicknesses that occursdepends on the pattern of the semiconductor wafer. Therefore, accordingto the invention, first, a correction value is calculated for unevenbrightness that occurs, in a spatially continuous area, that depends onthe pattern (S1083). Then, a correction value is calculated for unevenbrightness that occurs at random (S1084). Image comparison is performedby employing these correction values that are obtained hierarchicallyand image parameters, such as a threshold value, that are designated bythe parameter setup unit 110 in accordance with the individual detectionconditions (109).

The process in FIG. 12 is a process for correcting differences inbrightness that occur in individual areas. The spatially continuousareas are extracted from a detected image 1210 and a reference image1220 after the registration has been completed (1201), and a correctionvalue for brightness is calculated for each extracted area (1202). Basedon the obtained results, the correction of brightness is performed forthe individual areas (1203).

An example movement is shown in FIG. 28 when the correction ofbrightness is performed hierarchically according to the invention. Twodefects 2701 (portions enclosed by O) are present in a detected image(a), and are brighter than a background pattern having a belt shape, sothat they can be distinguished from the background pattern. However, acorresponding pattern 2702 in a reference image (b) has the samebrightness as a defect, and when a difference between the two images issimply calculated, a difference for the defect is small, as in adifferential image (c). When a scatter diagram (d) for the brightnessesof a detected image and a reference image are prepared, the defect cannot be identified from an evenly bright area. It should be noted that,in the scatter diagram (d), brightness is plotted while the verticalaxis represents the detected image and the horizontal axis representsthe reference image. Whereas, in this invention, as denoted by 2702, thepartial image is extracted from a target image in accordance with apattern or an area, and the correction value for the brightness iscalculated for each area (1202 in FIG. 12). An example method forcalculating a correction value is shown in (Ex. 1).

$\begin{matrix}{{E_{F} = {\frac{1}{\left( {N \times M} \right)}{\sum\limits_{N}\; {\sum\limits_{M}\; {F\left( {i,j} \right)}}}}}{E_{G} = {\frac{1}{\left( {N \times M} \right)}{\sum\limits_{N}\; {\sum\limits_{M}\; {G\left( {i,j} \right)}}}}}{\sigma_{F} = \sqrt{\frac{1}{\left( {N \times M} \right)}\left\{ {\sum\limits_{N}\; {\sum\limits_{M}\; \left( {{F\left( {i,j} \right)} - E_{F}} \right)^{2}}} \right\}}}{\sigma_{G} = \sqrt{\frac{1}{\left( {N \times M} \right)}\left\{ {\sum\limits_{N}\; {\sum\limits_{M}\; \left( {{G\left( {i,j} \right)} - E_{G}} \right)^{2}}} \right\}}}} & \left( {{Ex}.\mspace{14mu} 1} \right)\end{matrix}$

Wherein F(i, j) and G(i, j) denote brightnesses at positions (i, j) of adetected image and a reference image after registration has beenperformed. Then, a correction value is calculated by (Ex. 2).

gain=σ_(F)/σ_(G)

offset=E_(F)−gain·E _(G)  (Ex. 2)

Correction of the area is performed for the reference image by using(Ex. 3) (1203).

G(i, j)=gain·G(i, j)+offset  (Ex. 3)

At this time, as denoted by 1202 in FIG. 28, a histogram for a densitydifference may be formed for each area (1202-1), and an offset valuesuch that the peak position of the histogram is zero may be calculatedas a correction value for a corresponding area and be employed forcorrection (1202-2).

Next, a correction value, which is used to adjust a difference inbrightness that occurs at random, depending on the pattern of asemiconductor wafer, is calculated for each pixel. An example for thisprocessing is shown in FIGS. 14 and 15A to 15E.

First, the processing is shown in the flowchart in FIG. 14. For an imagefor which the brightness has been corrected for each area, as explainedat step 1203 in FIG. 12, a feature value for each pixel is calculatedand is mapped to the N-dimensional feature space with the feature valuebeing used as an axis (14-1) ((f) in FIG. 28). Then, the feature spaceis divided into segments (14-2), and a correction value is calculatedfor each of the obtained segments (14-3).

Next, the processing based on the flowchart shown in FIG. 14 is shown inFIGS. 15A to 15E. Reference numerals 1501 and 1502 in FIG. 15A denote areference image and a detected image that have been corrected at 1203,and an uneven brightness occurs at a repeated dot pattern. As a result,as shown in scatter diagram 15B, the distribution of data spreads, and adefect is not detected. For these images, feature values for theindividual pixels are calculated and are mapped to the feature space, asshown in FIG. 15C (corresponds to 14-1 in FIG. 14). In FIG. 15C, thetwo-dimensional feature space is shown as an example, and (f) in FIG. 28is an example wherein individual pixels are mapped in the N-dimensionalfeature space. The feature value can be an arbitrary thing, such aspixel contrast, brightness, a quadratic differential value, a densitydifference between corresponding pixels or a dispersion value using aneighboring pixel, so long as the feature for the pixel is represented.Further, the feature values may be mapped to a space wherein all thefeature values are employed, or feature values effective for determininga defect may be selected and mapped. Following this, the feature spaceis divided into a plurality of segments (14-2 in FIG. 14), and acorrection value is calculated for each segment by using the statisticamount of pixels belonging to the segment (14-3 in FIG. 14). FIGS. 15Dand 15E are scatter diagrams that are formed by extracting, from thescatter diagram in FIG. 15B for the entire image, pixels included in thesegments obtained from the feature space. Then, the correction value iscalculated based on the scatter diagrams for the individual segments inFIGS. 15D, 15E, . . . . As a result, as shown in a density differentialimage in (e) in FIG. 28, the uneven brightness of the dot pattern iscorrected, and a defect can be detected.

An example division method for dividing the feature space in FIG. 15Cinto segments is shown in FIGS. 16A and 16B. According to thisinvention, the feature space is automatically divided into segments inaccordance with a target image. The upper graph in FIG. 16A shows anexample feature space according to a brightness and density difference.The lower graph shows an example histogram indicating the frequency ofeach brightness level (luminance value), and a threshold value used fordivision in the direction of brightness is determined based on thehistogram of the brightness of the target image.

Example processing is shown in FIG. 17. First, a histogram for luminancevalues in a target area is calculated (17-1). The histogram may becalculated based on a detected image or a reference image, or based onthe average value of the two images. Then, the luminance histogram issmoothed and small peaks are removed (17-2), and differential values forthe smoothed histogram are calculated (17-3). Thereafter, thedifferential values are examined, beginning from the lowest brightness,and the luminance value whereat the differential value is positive isdefined as Start, while the luminance value whereat the nextdifferential value is negative is defined as End (17-4). And theluminance value for which the differential value is the maximum in therange from Start to End is defined as a threshold value used fordivision (17-5). Through this processing, as shown in FIG. 16A, thedivision is performed at the portion for the valley of the histogram.This means that the segment division is performed in accordance with thepattern in the target area. The segment division can be performed eitherin accordance with the pattern in the image, or, as shown in FIG. 16B,in accordance with a fixed value designated by a user.

In FIGS. 27A to 27G, an example movement in the processing in FIG. 14for calculating a correction value for each pixel is shown by employingscatter diagrams. A large brightness difference exists between adetected image (FIG. 27A) and a reference image (FIG. 27B), and as shownin a differential image (FIG. 27C), this difference is increased. FIG.27D is a scatter diagram between FIGS. 27A and 27B. This diagram isexploded in accordance with the feature values, as shown in FIG. 27E,and a correction value is calculated for each scatter diagram and acorrection is performed. Then, as shown in FIG. 27F, data distributionin the scatter diagram between FIGS. 27A and 27B is suppressed, and asshown in a differential image (FIG. 27G) obtained by a correction, thedifference is reduced.

For each of the scatter diagrams obtained by segment division, as shownin FIGS. 15D and 15E, a linear formula is obtained using theleast-squares approximation in the scatter diagram, and the inclinationand the y intercept are regarded as correction values. The correctionvalues may be obtained using (Ex. 1) and (Ex. 2), described above, basedon the pixels belonging to each segment. Further, an area that forms thefeature space may be arbitrarily designated as the minimum 1×1 pixel. Itshould be noted, however, that when the 1×1 pixel that has the highestfrequency is employed, the correction would be performed, including fora defect, so that a slightly larger area should be designated.

As is described above, (1) since the brightness is corrected for eacharea, a defect hidden by the uneven brightness of the background isrevealed; (2) since brightness is corrected for each pixel, an unevenbrightness that occurs at random is suppressed; and since hierarchicalbrightness is corrected, only a defect can be detected ((e) in FIG.28)).

Brightness correction results obtained using the method of the inventionare shown in FIGS. 18A to 18E. A differential image obtained afterregistration is shown in FIG. 18A, and defects are present at positionsenclosed by O. The luminance waveforms for two images, including defectportions, after registration at 1D-1D′ and a difference value are shownin FIG. 18D. A defect is present in the detected image, and that portionis brighter than the other portion, while the reference image is brightas a whole, and the difference value for the defect portion is smallerthan the peripheral portion. As a comparison, a differential imageobtained by correcting differences in brightness for the individualareas is shown in FIG. 18B. The uneven brightnesses having a belt shapeare extracted as areas and are corrected. As a result, as shown in FIG.18E, the luminance values are adjusted and a defect hidden in the unevencolor of the background is revealed. However, a difference in brightnessthat occurs at random, depending on the repeated pattern, is not yetcorrected.

In the example in FIG. 18C, a correction value for each pixel iscalculated based on the statistical amount of the entire image area inFIG. 18B and the differences in brightness are corrected, and anobtained differential image is shown. In this manner, since two or moredifferent processing methods are employed to hierarchically calculate abrightness correction value, the differences in brightness that arecaused by different conditions can be corrected, and a defect for whicha weak signal is hidden in a large uneven brightness can be revealed anddetected.

The present invention can cope with a case wherein a larger unevenbrightness is present. In a detected image in FIG. 19A and a referenceimage in FIG. 19B, uneven brightness exists in a background 1901, and adifference in a portion corresponding to 1901 is increased in adifferential image (FIG. 19C). As for the distribution of data in ascatter diagram between FIGS. 19A and 19B, a part of a brightness areaspreads, as shown in FIG. 19D. Whereas, as shown in FIG. 13, positionshifting is normally performed before the correction of unevenbrightness, a correct position shift distance may not be calculated whenthe difference in brightness is great. On the other hand, when theposition shifting occurs, a correlation of pixels can not be obtainedbetween images, and an exact brightness correction value can not becalculated. Therefore, the present invention provides a unit thatadjusts the position shift and the brightness difference.

Examples in FIGS. 20A to 20E show larger uneven brightnesses than thosein FIGS. 19A to 19D. A background 2001 in FIG. 20A is brighter; however,a pattern 2002 that is overlaid has almost no difference in brightness.As for patterns 2003 thereon, some are brighter while the others aredark in FIG. 20A, and the brightness is inverted relative to that inFIG. 20B. The scatter diagram for such an image (FIGS. 20A and 20B)shows a positive correlation and a negative correlation, as shown inFIG. 20D. However, using the method for employing the square sum of thedensity difference to calculate the position shift distance, so that thebrightness difference is a minimum value, a correct position shiftdistance can not be obtained. Further, using a method for employing anormalized correlation robust enough for uneven brightness, calculationof a correct position shift distance is impossible for an image whereinboth a positive correlation and a negative correlation exist. Acorrelation coefficient between FIGS. 20A and 20B is shown in FIG. 20E,and it indicates that a value is small as a whole and that a positionshift distance can not be calculated.

Therefore, according to this invention, as shown in FIG. 21A, theposition correction value is allocated ((αi, βi)) for the periphery byusing the position shift distance (α, β) of the preceding unit, and theposition correction (S211)→calculation of the uneven brightnesscorrection value (S212)→adjustment of brightness (S213)→evaluation ofthe scatter diagram (S214) are repeated. Then, as shown in FIG. 21B, ofthe scatter diagram data (a-1), (a-i) and (a-n) in the individualposition correction processes, (a-i) with the smallest discrepancies isemployed to perform the inspection. The discrepancies in scatter diagramdata are calculated by using the square sum of the difference in thepixels.

At this time, as shown in FIGS. 29A and 29B, first, the scatter diagrammay be exploded in accordance with the feature values of the pixels inthe manner as shown in FIGS. 15A to 15E, and the processing in FIGS. 21Aand 21B may be repeated for each scatter diagram. That is, the positionshift distance is allocated ((αi, βi)) for each scatter diagram, and theposition correction (2901)→calculation of an uneven brightnesscorrection value for a corresponding pixel (2902)→correction ofbrightness (2903)→evaluation of a scatter diagram (2904) are repeated.Then, a position shift distance ((αi, βi), and a brightness correctionvalue, whereby data in a scatter diagram are spread the least, i.e., forwhich the sum of the difference between corresponding pixels of imagesis the minimum, are employed (2905).

As another method, as shown in FIG. 22, a detected image 2201 that is atarget image and a reference image 2202 are passed through adifferential filter (S221), and the edges of patterns are detected inthe image (S222). A position correction value (α, β) is calculated byemploying only the pixels that correspond to the edges (S223) and isemployed to correct the positions (S224), and an image F′ (2203) and animage G′ (2204) are obtained. As a result, a position shift distance canbe calculated while the affect of an uneven brightness is removed, andby using these results, an uneven brightness correction value can becalculated (S225). In the detection of edges at (S222), pixels for whichthe brightness gradient is sharp between adjacent pixels, i.e., pixelswith large linear differential values or pixels with zero-crossing ofquadratic differential values should be selected.

Selection of pixels effective for calculation of a position shiftdistance is not limited to that for the edges where the brightnessgradient is large. As shown in FIG. 32, for pixels of a detected image2201 and a reference image 2202, a difference between adjacent pixels iscalculated (S321) and a symbol array of the difference values isobtained (S322). For simplification, when image data in this case isregarded as a one-dimensional array, differences df(i) and dg(i) betweenpixels f(i) and g(i) of a detected image and a reference image andadjacent pixels are calculated as in (Ex. 4).

df(i)=f(i+1)−f(i)

dg(i)=g(i+1)−g(i)  (Ex. 4)

Symbol arrays cf(i) and cg(i) are obtained

if df(i)≧0 then cf(i)=1 else cf(i)=0

if dg(i)≧0 then cg(i)=1 else cg(i)=0

Thereafter, pixels for which the symbol array match, includingneighboring areas (because, originally, a position shift exists betweenimages) are selected, a position correction distance (α, β) iscalculated (S323), and position correction is performed (S324). As aresult, a pattern wherein brightness is partially inverted can beremoved, and position correction can be performed.

Furthermore, as an index for the selection of pixels that are effectivefor the calculation of a position shift value, as shown in FIG. 33,dispersion values fs and gs may be calculated by using (Ex. 5) inaccordance with n neighboring pixels f(i) and g(i) of pixels f and g ina detected image 2201 and a reference image 2202 (S331).

$\begin{matrix}{{{fs} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; \left( {{f(i)} - \overset{\_}{f}} \right)}}}{{gs} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; \left( {{g(i)} - \overset{\_}{g}} \right)}}}} & \left( {{Ex}.\mspace{14mu} 5} \right)\end{matrix}$

Then, pixels near the two values may be selected, to calculate aposition correction value (α, β) (S333), and the position correction maybe performed (S334).

f, g  (Ex. 6)

In this case, expression (6) represents the average luminance of the nneighboring pixels.

The example pixel selection method for calculating the position shiftdistance has been described. However, another selection index may beemployed. The present invention selects pixels by using an arbitraryindex, in other words, removes pixels that adversely affect thecalculation of the position shift distance and ensures accuratecalculation of the position shift distance.

As another method, as shown in FIG. 23, areas are extracted from adetected image 2301 and a reference image 2302, and position correctionvalue calculation (S232), position correction (S233), calculation of anuneven brightness correction value (S234) and correction of unevenbrightnesses (S235) are performed for corresponding areas. As a result,a position shift correction value and a brightness correction value canbe calculated separately for a portion wherein a positive correlationexists and a portion wherein a negative correlation exists.

As an additional method, as shown in FIG. 24, areas are extracted from adetected image 2401 and a reference image 2402 (S241), a positioncorrection value and a brightness correction value are allocated asparameters for corresponding areas, and a position correction value anda brightness correction value, the square sum of the difference of whichis the minimum, are employed (S242).

As one more method, as shown in FIG. 30, area extraction and explosionof a scatter diagram, based on the feature values for individual pixels,are performed for a detected image 3001 and a reference image 3002(S301). Then, a position correction value and a brightness correctionvalue are allocated as parameters for area information and theindividual scatter diagrams that are obtained based on the featurevalues, and a position correction value and a brightness correctionvalue, the square sum of the difference of which is the minimum, areemployed (S302).

As a further method, as shown in FIGS. 25A to 25D, the scatter diagram(FIG. 25A) is changed by Hough conversion, and distribution data areseparated into data for a negative correlation and data for a positivecorrelation (FIGS. 25B to 25D). Thereafter, for the separateddistribution data, a position correction value and a brightnesscorrection value are calculated so that the discrepancies in the scatterdiagram data are the minimum.

The effects obtained by performing brightness correction for aninspection image at multiple steps, in the manner described above, willnow be explained for a case, for example, of inspecting a pattern thatis formed on a semiconductor wafer wherein, through the CMP step, thesurface is covered with an optically transparent, flat insulating film.When the wafer, after being CMP machined, is photographed by thedetector 13, the obtained image is affected, for example, by variancesin the thickness of the insulating film on the wafer plane and thedistribution of the amount of reflected light that occurs due to thedensity of the patterns in the chip, and the brightness is varied. Forthe image, wherein the brightness is varied, brightness correction isperformed at multiple steps, for different units and using the methoddescribed above, so that the affect of the uneven brightnesses betweenimages can be reduced and a defect can be revealed. Therefore, a ratiofor the detection of defects can be increased.

Further, a differences in brightness (uneven color) between chips, whichoccurs due to various factors, such as the difference in filmthicknesses between chips, differences in the amount of accumulatedlight due to the uneven speed of the stage or the fluctuation ofillumination, is adjusted by using a plurality of different methods(i.e., correction values are calculated for a plurality of differentareas). Thus, a defect for which a weak signal is hidden in large unevenbrightnesses can be revealed and be detected.

The above described processing performed by the image comparator 15 ofthe invention can be provided by software for a CPU. Or the corecomputation portions, such as the normalized correlation computation andthe shaping of the feature space, may be provided by hardware. With thisarrangement, the processing speed can be increased. Further, accordingto the invention, although following the CMP planarization process thereis a slight difference in the pattern thicknesses, or although there isa large difference in brightness between comparison dies due to areduction in the wavelength of illuminating light, defects of 20 nm to90 nm can be detected.

Furthermore, according to the invention, during the inspection of low kfilms, like inorganic insulating films such as SiO₂, SiOF, BSG, SiOB orporous silia film, or like organic insulating films such as methylcontaining SiO₂, MSQ, polyimido film, palerin film, Teflon (trademark)film or amorphous carbon film, defects of 20 nm to 90 nm can bedetected, even though there are local differences in brightness due tovariations in the refractive index distribution in the film.

An example semiconductor apparatus manufacturing system that employs thepattern inspection apparatus of the present invention will now bedescribed while referring to FIG. 31. A circuit pattern on a wafer isformed sequentially by a plurality of apparatuses A, B . . . and E(3101). After the manufacturing apparatuses have completed theirprocesses, the inspection and defect categorizing are performed by thepattern inspection apparatus of this invention, so that a defect type,an apparatus wherein the defect occurred and how the defect occurred canbe traced. For example, a wafer processed through the step at theapparatus B is inspected, and a defect that is detected is a defectcarried from the preceding apparatus A or a defect that occurred in theapparatus B. When the defect is compared with defect data obtainedthrough the inspection following the step by the apparatus A, it isdetermined whether the defect is either a defect that occurred in theapparatus B or a defect carried by the apparatus A (3102). Of thedefects carried from the preceding apparatus A, a foreign substance maynot cause a shape defect or discoloration in the apparatus B, and apattern to which a fatal substance is attached is always defective inshape or is discolored in the following apparatus, while a pattern towhich a non-fatal substance is attached does not become defective inshape in the following apparatus and is still a good product. Therefore,by using the pattern inspection apparatus with this configuration, thecoordinates of a the defect categorized as a foreign substance arestored, and when the pattern is passed through the succeeding apparatus,the defect detection and categorizing are again performed to determinewhether the foreign substance is fatal. Through this processing, thestate of the manufacturing apparatus can be more exactly understood.

The embodiment of the present invention has been explained by employing,as an example, a comparison of inspection images using an optical visualinspection apparatus that employs a semiconductor wafer as an object.However, the present invention can also be applied for images to becompared for an electron pattern inspection. Further, the inspectionobject is not limited to a semiconductor wafer, and so long as thedetection of a defect is performed by a comparison of images, thepresent invention can also be applied for a TFT substrate, a photomaskor a print board.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims rather than by the foregoing description and all changeswhich come within the meaning and range of equivalency of the claims aretherefore intended to be embraced therein.

1. A pattern inspection method comprising the steps of: irradiatingcorresponding areas of two identical patterns that are formed on asample, while adjusting a ratio of light quantities emitted obliquelyand from above; photographing the corresponding areas of the twopatterns irradiated by adjusting the ratio of illuminating lightquantities; processing images obtained for the corresponding areas ofthe two patterns and detecting a defect utilizing a selected processingalgorithm; categorizing the detected defect, and displaying thecategorizing results on a screen.
 2. A pattern inspection apparatuscomprising: an oblique illumination optical system for obliquelyilluminating a sample; a downward illumination optical system forilluminating the sample from above; a light quantity ratio adjustmentunit for adjusting a ratio of light quantities for the obliqueillumination optical system and the downward illumination opticalsystem; an optical imaging unit for collecting light reflected from thesample that is illuminated by the oblique illumination optical systemand the downward illumination optical system for which the lightquantity ratio is adjusted by the light quantity ratio adjustment unit;a photographing unit for photographing an optical image of the reflectedlight that is collected by the optical imaging unit; an image processingunit which utilizes a selected image processing algorithms, an imageprocessing algorithm that corresponds to a light quantity ratio that isadjusted by a light quantity adjustment section of the illumination unitand processing an image obtained by the photographing unit, and fordetecting and categorizing a defect; and a display unit for displayingresults obtained by the image processing unit.